# Accommodating Uncertainty in Prior Distributions

## Abstract

A fundamental premise of Bayesian methodology is that a priori information is accurately summarized by a single, precisely de ned prior distribution. In many cases, especially involving informative priors, this premise is false, and the (mis)application of Bayes methods produces posterior quantities whose apparent precisions are highly misleading. We examine the implications of uncertainty in prior distributions, and present graphical methods for dealing with them.

- Authors:

- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

- Publication Date:

- Research Org.:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)

- OSTI Identifier:
- 1340952

- Report Number(s):
- LA-UR-17-20370

- DOE Contract Number:
- AC52-06NA25396

- Resource Type:
- Technical Report

- Country of Publication:
- United States

- Language:
- English

- Subject:
- 97 MATHEMATICS AND COMPUTING; Bayesian sensitivity analysis; imprecise probabilities; informative priors

### Citation Formats

```
Picard, Richard Roy, and Vander Wiel, Scott Alan.
```*Accommodating Uncertainty in Prior Distributions*. United States: N. p., 2017.
Web. doi:10.2172/1340952.

```
Picard, Richard Roy, & Vander Wiel, Scott Alan.
```*Accommodating Uncertainty in Prior Distributions*. United States. doi:10.2172/1340952.

```
Picard, Richard Roy, and Vander Wiel, Scott Alan. Thu .
"Accommodating Uncertainty in Prior Distributions". United States. doi:10.2172/1340952. https://www.osti.gov/servlets/purl/1340952.
```

```
@article{osti_1340952,
```

title = {Accommodating Uncertainty in Prior Distributions},

author = {Picard, Richard Roy and Vander Wiel, Scott Alan},

abstractNote = {A fundamental premise of Bayesian methodology is that a priori information is accurately summarized by a single, precisely de ned prior distribution. In many cases, especially involving informative priors, this premise is false, and the (mis)application of Bayes methods produces posterior quantities whose apparent precisions are highly misleading. We examine the implications of uncertainty in prior distributions, and present graphical methods for dealing with them.},

doi = {10.2172/1340952},

journal = {},

number = ,

volume = ,

place = {United States},

year = {2017},

month = {1}

}

Save to My Library

You must Sign In or Create an Account in order to save documents to your library.